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Flow Control. An Engineering Approach to Computer Networking. Flow control problem. Consider file transfer Sender sends a stream of packets representing fragments of a file Sender should try to match rate at which receiver and network can process data Can’t send too slow or too fast

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flow control

Flow Control

An Engineering Approach to Computer Networking

flow control problem
Flow control problem
  • Consider file transfer
  • Sender sends a stream of packets representing fragments of a file
  • Sender should try to match rate at which receiver and network can process data
  • Can’t send too slow or too fast
  • Too slow
    • wastes time
  • Too fast
    • can lead to buffer overflow
  • How to find the correct rate?
other considerations
Other considerations
  • Simplicity
  • Overhead
  • Scaling
  • Fairness
  • Stability
  • Many interesting tradeoffs
    • overhead for stability
    • simplicity for unfairness
where
Where?
  • Usually at transport layer
  • Also, in some cases, in datalink layer
model
Model
  • Source, sink, server, service rate, bottleneck, round trip time
classification
Classification
  • Open loop
    • Source describes its desired flow rate
    • Network admits call
    • Source sends at this rate
  • Closed loop
    • Source monitors available service rate
      • Explicit or implicit
    • Sends at this rate
    • Due to speed of light delay, errors are bound to occur
  • Hybrid
    • Source asks for some minimum rate
    • But can send more, if available
open loop flow control
Open loop flow control
  • Two phases to flow
    • Call setup
    • Data transmission
  • Call setup
    • Network prescribes parameters
    • User chooses parameter values
    • Network admits or denies call
  • Data transmission
    • User sends within parameter range
    • Network polices users
    • Scheduling policies give user QoS
hard problems
Hard problems
  • Choosing a descriptor at a source
  • Choosing a scheduling discipline at intermediate network elements
  • Admitting calls so that their performance objectives are met (call admission control).
traffic descriptors
Traffic descriptors
  • Usually an envelope
    • Constrains worst case behavior
  • Three uses
    • Basis for traffic contract
    • Input to regulator
    • Input to policer
descriptor requirements
Descriptor requirements
  • Representativity
    • adequately describes flow, so that network does not reserve too little or too much resource
  • Verifiability
    • verify that descriptor holds
  • Preservability
    • Doesn’t change inside the network
  • Usability
    • Easy to describe and use for admission control
examples
Examples
  • Representative, verifiable, but not useble
    • Time series of interarrival times
  • Verifiable, preservable, and useable, but not representative
    • peak rate
some common descriptors
Some common descriptors
  • Peak rate
  • Average rate
  • Linear bounded arrival process
peak rate
Peak rate
  • Highest ‘rate’ at which a source can send data
  • Two ways to compute it
  • For networks with fixed-size packets
    • min inter-packet spacing
  • For networks with variable-size packets
    • highest rate over all intervals of a particular duration
  • Regulator for fixed-size packets
    • timer set on packet transmission
    • if timer expires, send packet, if any
  • Problem
    • sensitive to extremes
average rate
Average rate
  • Rate over some time period (window)
  • Less susceptible to outliers
  • Parameters: t and a
  • Two types: jumping window and moving window
  • Jumping window
    • over consecutive intervals of length t, only a bits sent
    • regulator reinitializes every interval
  • Moving window
    • over all intervals of length t, only a bits sent
    • regulator forgets packet sent more than t seconds ago
linear bounded arrival process
Linear Bounded Arrival Process
  • Source bounds # bits sent in any time interval by a linear function of time
  • the number of bits transmitted in any active interval of length t is less than rt + s
  • r is the long term rate
  • s is the burst limit
  • insensitive to outliers
leaky bucket
Leaky bucket
  • A regulator for an LBAP
  • Token bucket fills up at rate r
  • Largest # tokens < s
variants
Variants
  • Token and data buckets
    • Sum is what matters
  • Peak rate regulator
choosing lbap parameters
Choosing LBAP parameters
  • Tradeoff between r and s
  • Minimal descriptor
    • doesn’t simultaneously have smaller r and s
    • presumably costs less
  • How to choose minimal descriptor?
  • Three way tradeoff
    • choice of s (data bucket size)
    • loss rate
    • choice of r
choosing minimal parameters
Choosing minimal parameters
  • Keeping loss rate the same
    • if s is more, r is less (smoothing)
    • for each r we have least s
  • Choose knee of curve
slide20
LBAP
  • Popular in practice and in academia
    • sort of representative
    • verifiable
    • sort of preservable
    • sort of usable
  • Problems with multiple time scale traffic
    • large burst messes up things
open loop vs closed loop
Open loop vs. closed loop
  • Open loop
    • describe traffic
    • network admits/reserves resources
    • regulation/policing
  • Closed loop
    • can’t describe traffic or network doesn’t support reservation
    • monitor available bandwidth
      • perhaps allocated using GPS-emulation
    • adapt to it
    • if not done properly either
      • too much loss
      • unnecessary delay
taxonomy
Taxonomy
  • First generation
    • ignores network state
    • only match receiver
  • Second generation
    • responsive to state
    • three choices
      • State measurement
        • explicit or implicit
      • Control
        • flow control window size or rate
      • Point of control
        • endpoint or within network
explicit vs implicit
Explicit vs. Implicit
  • Explicit
    • Network tells source its current rate
    • Better control
    • More overhead
  • Implicit
    • Endpoint figures out rate by looking at network
    • Less overhead
  • Ideally, want overhead of implicit with effectiveness of explicit
flow control window
Flow control window
  • Recall error control window
  • Largest number of packet outstanding (sent but not acked)
  • If endpoint has sent all packets in window, it must wait => slows down its rate
  • Thus, window provides both error control and flow control
  • This is called transmission window
  • Coupling can be a problem
    • Few buffers are receiver => slow rate!
window vs rate
Window vs. rate
  • In adaptive rate, we directly control rate
  • Needs a timer per connection
  • Plusses for window
    • no need for fine-grained timer
    • self-limiting
  • Plusses for rate
    • better control (finer grain)
    • no coupling of flow control and error control
  • Rate control must be careful to avoid overhead and sending too much
hop by hop vs end to end
Hop-by-hop vs. end-to-end
  • Hop-by-hop
    • first generation flow control at each link
      • next server = sink
    • easy to implement
  • End-to-end
    • sender matches all the servers on its path
  • Plusses for hop-by-hop
    • simpler
    • distributes overflow
    • better control
  • Plusses for end-to-end
    • cheaper
on off
On-off
  • Receiver gives ON and OFF signals
  • If ON, send at full speed
  • If OFF, stop
  • OK when RTT is small
  • What if OFF is lost?
  • Bursty
  • Used in serial lines or LANs
stop and wait
Stop and Wait
  • Send a packet
  • Wait for ack before sending next packet
static window
Static window
  • Stop and wait can send at most one pkt per RTT
  • Here, we allow multiple packets per RTT (= transmission window)
what should window size be
What should window size be?
  • Let bottleneck service rate along path = b pkts/sec
  • Let round trip time = R sec
  • Let flow control window = w packet
  • Sending rate is w packets in R seconds = w/R
  • To use bottleneck w/R > b => w > bR
  • This is the bandwidth delay product or optimal window size
static window1
Static window
  • Works well if b and R are fixed
  • But, bottleneck rate changes with time!
  • Static choice of w can lead to problems
    • too small
    • too large
  • So, need to adapt window
  • Always try to get to the current optimal value
decbit flow control
DECbit flow control
  • Intuition
    • every packet has a bit in header
    • intermediate routers set bit if queue has built up => source window is too large
    • sink copies bit to ack
    • if bits set, source reduces window size
    • in steady state, oscillate around optimal size
decbit
DECbit
  • When do bits get set?
  • How does a source interpret them?
decbit details router actions
DECbit details: router actions
  • Measure demand and mean queue length of each source
  • Computed over queue regeneration cycles
  • Balance between sensitivity and stability
router actions
Router actions
  • If mean queue length > 1.0
    • set bits on sources whose demand exceeds fair share
  • If it exceeds 2.0
    • set bits on everyone
    • panic!
source actions
Source actions
  • Keep track of bits
  • Can’t take control actions too fast!
  • Wait for past change to take effect
  • Measure bits over past + present window size
  • If more than 50% set, then decrease window, else increase
  • Additive increase, multiplicative decrease
evaluation
Evaluation
  • Works with FIFO
    • but requires per-connection state (demand)
  • Software
  • But
    • assumes cooperation!
    • conservative window increase policy
tcp flow control
TCP Flow Control
  • Implicit
  • Dynamic window
  • End-to-end
  • Very similar to DECbit, but
    • no support from routers
    • increase if no loss (usually detected using timeout)
    • window decrease on a timeout
    • additive increase multiplicative decrease
tcp details
TCP details
  • Window starts at 1
  • Increases exponentially for a while, then linearly
  • Exponentially => doubles every RTT
  • Linearly => increases by 1 every RTT
  • During exponential phase, every ack results in window increase by 1
  • During linear phase, window increases by 1 when # acks = window size
  • Exponential phase is called slow start
  • Linear phase is called congestion avoidance
more tcp details
More TCP details
  • On a loss, current window size is stored in a variable called slow start threshold or ssthresh
  • Switch from exponential to linear (slow start to congestion avoidance) when window size reaches threshold
  • Loss detected either with timeout or fast retransmit (duplicate cumulative acks)
  • Two versions of TCP
    • Tahoe: in both cases, drop window to 1
    • Reno: on timeout, drop window to 1, and on fast retransmit drop window to half previous size (also, increase window on subsequent acks)
tcp vs decbit
TCP vs. DECbit
  • Both use dynamic window flow control and additive-increase multiplicative decrease
  • TCP uses implicit measurement of congestion
    • probe a black box
  • Operates at the cliff
  • Source does not filter information
evaluation1
Evaluation
  • Effective over a wide range of bandwidths
  • A lot of operational experience
  • Weaknesses
    • loss => overload? (wireless)
    • overload => self-blame, problem with FCFS
    • ovelroad detected only on a loss
      • in steady state, source induces loss
    • needs at least bR/3 buffers per connection
tcp vegas
TCP Vegas
  • Expected throughput = transmission_window_size/propagation_delay
  • Numerator: known
  • Denominator: measure smallest RTT
  • Also know actual throughput
  • Difference = how much to reduce/increase rate
  • Algorithm
    • send a special packet
    • on ack, compute expected and actual throughput
    • (expected - actual)* RTT packets in bottleneck buffer
    • adjust sending rate if this is too large
  • Works better than TCP Reno
netblt
NETBLT
  • First rate-based flow control scheme
  • Separates error control (window) and flow control (no coupling)
  • So, losses and retransmissions do not affect the flow rate
  • Application data sent as a series of buffers, each at a particular rate
  • Rate = (burst size + burst rate) so granularity of control = burst
  • Initially, no adjustment of rates
  • Later, if received rate < sending rate, multiplicatively decrease rate
  • Change rate only once per buffer => slow
packet pair
Packet pair
  • Improves basic ideas in NETBLT
    • better measurement of bottleneck
    • control based on prediction
    • finer granularity
  • Assume all bottlenecks serve packets in round robin order
  • Then, spacing between packets at receiver (= ack spacing) = 1/(rate of slowest server)
  • If all data sent as paired packets, no distinction between data and probes
  • Implicitly determine service rates if servers are round-robin-like
packet pair details
Packet-pair details
  • Acks give time series of service rates in the past
  • We can use this to predict the next rate
  • Exponential averager, with fuzzy rules to change the averaging factor
  • Predicted rate feeds into flow control equation
packet pair flow control
Packet-pair flow control
  • Let X = # packets in bottleneck buffer
  • S = # outstanding packets
  • R = RTT
  • b = bottleneck rate
  • Then, X = S - Rb (assuming no losses)
  • Let l = source rate
  • l(k+1) = b(k+1) + (setpoint -X)/R
atm forum eerc
ATM Forum EERC
  • Similar to DECbit, but send a whole cell’s worth of info instead of one bit
  • Sources periodically send a Resource Management (RM) cell with a rate request
    • typically once every 32 cells
  • Each server fills in RM cell with current share, if less
  • Source sends at this rate
atm forum eerc details
ATM Forum EERC details
  • Source sends Explicit Rate (ER) in RM cell
  • Switches compute source share in an unspecified manner (allows competition)
  • Current rate = allowed cell rate = ACR
  • If ER > ACR then ACR = ACR + RIF * PCR else ACR = ER
  • If switch does not change ER, then use DECbit idea
    • If CI bit set, ACR = ACR (1 - RDF)
  • If ER < AR, AR = ER
  • Allows interoperability of a sort
  • If idle 500 ms, reset rate to Initial cell rate
  • If no RM cells return for a while, ACR *= (1-RDF)
comparison with decbit
Comparison with DECbit
  • Sources know exact rate
  • Non-zero Initial cell-rate => conservative increase can be avoided
  • Interoperation between ER/CI switches
problems
Problems
  • RM cells in data path a mess
  • Updating sending rate based on RM cell can be hard
  • Interoperability comes at the cost of reduced efficiency (as bad as DECbit)
  • Computing ER is hard
comparison among closed loop schemes
Comparison among closed-loop schemes
  • On-off, stop-and-wait, static window, DECbit, TCP, NETBLT, Packet-pair, ATM Forum EERC
  • Which is best? No simple answer
  • Some rules of thumb
    • flow control easier with RR scheduling
      • otherwise, assume cooperation, or police rates
    • explicit schemes are more robust
    • hop-by-hop schemes are more resposive, but more comples
    • try to separate error control and flow control
    • rate based schemes are inherently unstable unless well-engineered
hybrid flow control
Hybrid flow control
  • Source gets a minimum rate, but can use more
  • All problems of both open loop and closed loop flow control
  • Resource partitioning problem
    • what fraction can be reserved?
    • how?
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